I am at Axeda’s annual Connexion conference which brings together users, practitioners, analysts and providers in the M2M (machine-to-machine) / connected products ecosystem and found an interesting outcome of connecting the physical, digital and data networks of supply chain fulfillment: on-demand ice cream… with toppings no less.

MooBella (www.MooBella.com) is showcasing machines that enable the user to configure their own ice cream by selecting a base, from a variety of flavors and from a selection of toppings. Once selected, your exact order is served on-demand. It’s an easy three step process. (1) Select your Ice Cream, (2) Pick your flavor and (3) Add your mix-in. The “user interface” is shown here:

While that capability alone is impressive, it is the inner-workings of the machine that grabbed my attention. The ice cream bases (premium or low fat) stored separately from the flavorings in the lower cabinet. This base travels via separate tubes into the chamber above where the different flavor options are added via the user’s specifications. As the photo below shows, many different flavor offerings are available along with candy and cookie toppings.

These inner-workings make up the physical fulfillment of the product to a user. A physical supply chain. It is, however, the data generated in the multiple transactions that differentiates this machine from the rest of market. This data can used both to optimize the performance of a single machine as well as aggregated to optimize the entire network of products. For example…

Single machine: The embedded technology within the dispenser can monitor both performance as well as usage. Data on the performance is shared with the services organization to ensure the machine is always running and enables remote services so that any issues can be proactively addressed before the issue ever impacts a user. Usage data is shared to show what flavors are most often being selected, how long it takes to complete an order and the volume of users. This data can be used to optimize replenishment as well as offerings at the unit level. A demo of the internal monitoring is shown here:

Extended network: With an aggregated view of all the data across every deployed machine, MooBella can enhance their customers’ experiences by optimizing offerings to maximize availability of the most popular flavors, by reducing cycle times in the user experience and by streamlining offerings to minimize queue times in the busiest locations (like a sports arena).

So what does any of this have to do with supply chain? The answers all circulate around the integration of data with the physical supply chain.

Demand management: MooBella’s machines are a clear example of a semi-configurable make-to-order process. These types of processes often lead to variability that will meals demand management more difficult. Analysis of usage patterns will enable better supply alignment with a better understanding of short and long-term demand. Offerings per machine can also delimited where needed to actually shape demand.

Supply chain services: With a remote diagnostic of any issues, the services organization can align repairs and service parts management in a more efficient manner. Not only does this reduce costs associated with downtime and multiple repairs, it ultimately delights customers by avoiding any disruptions to their use of the product.

Cycle time management: Data analysis on the time it takes to start an order to get your ice cream is monitored as an indication of how difficult it is to complete an order. This time can vary, say between an elementary school and a corporate office (the kids most likely being much more adept with the technology). Based on this data, offerings will be aligned at the machine level to ensure the supply matches the individual demand. This same methodology can be used to reduce the time per order in high volume areas.

Is M2M here to stay or is it a fad? How will this type of technology impact supply chain? Share your thoughts…

Thoughts on M2M Connected Supply Chain … for Ice Cream?

When you consider that the analysts can forecast the winner of a presidential election with a relatively small sample, no reason to think that actual consumption data from a set of representative machines couldn’t be a fantastic production or supply chain forecasting tool!

Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.